Employee Salary Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a34e33a",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8821423c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8e2278",
   "metadata": {},
   "source": [
    "# Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e813789",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee6f3207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_then_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  salary_more_then_100k\n",
       "0       google      sales executive  bachelors                      0\n",
       "1       google      sales executive    masters                      0\n",
       "2       google     business manager  bachelors                      1\n",
       "3       google     business manager    masters                      1\n",
       "4       google  computer programmer  bachelors                      0\n",
       "5       google  computer programmer    masters                      1\n",
       "6   abc pharma      sales executive    masters                      0\n",
       "7   abc pharma  computer programmer  bachelors                      0\n",
       "8   abc pharma     business manager  bachelors                      0\n",
       "9   abc pharma     business manager    masters                      1\n",
       "10    facebook      sales executive  bachelors                      1\n",
       "11    facebook      sales executive    masters                      1\n",
       "12    facebook     business manager  bachelors                      1\n",
       "13    facebook     business manager    masters                      1\n",
       "14    facebook  computer programmer  bachelors                      1\n",
       "15    facebook  computer programmer    masters                      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde04668",
   "metadata": {},
   "source": [
    "# X and y matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5f7ff87",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('salary_more_then_100k',axis=1)\n",
    "y = data['salary_more_then_100k']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a15757",
   "metadata": {},
   "source": [
    "# Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248732f2",
   "metadata": {},
   "source": [
    "## Normalizing non-numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c48bd67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le_company = LabelEncoder()\n",
    "le_job = LabelEncoder()\n",
    "le_degree = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dcaab4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>abc pharma</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>facebook</td>\n",
       "      <td>sales executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>facebook</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>facebook</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company                  job     degree  company_n  job_n  degree_n\n",
       "0       google      sales executive  bachelors          2      2         0\n",
       "1       google      sales executive    masters          2      2         1\n",
       "2       google     business manager  bachelors          2      0         0\n",
       "3       google     business manager    masters          2      0         1\n",
       "4       google  computer programmer  bachelors          2      1         0\n",
       "5       google  computer programmer    masters          2      1         1\n",
       "6   abc pharma      sales executive    masters          0      2         1\n",
       "7   abc pharma  computer programmer  bachelors          0      1         0\n",
       "8   abc pharma     business manager  bachelors          0      0         0\n",
       "9   abc pharma     business manager    masters          0      0         1\n",
       "10    facebook      sales executive  bachelors          1      2         0\n",
       "11    facebook      sales executive    masters          1      2         1\n",
       "12    facebook     business manager  bachelors          1      0         0\n",
       "13    facebook     business manager    masters          1      0         1\n",
       "14    facebook  computer programmer  bachelors          1      1         0\n",
       "15    facebook  computer programmer    masters          1      1         1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['company_n'] = le_company.fit_transform(X['company'])\n",
    "X['job_n'] = le_job.fit_transform(X['job'])\n",
    "X['degree_n'] = le_degree.fit_transform(X['degree'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c6aa55b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_n</th>\n",
       "      <th>job_n</th>\n",
       "      <th>degree_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    company_n  job_n  degree_n\n",
       "0           2      2         0\n",
       "1           2      2         1\n",
       "2           2      0         0\n",
       "3           2      0         1\n",
       "4           2      1         0\n",
       "5           2      1         1\n",
       "6           0      2         1\n",
       "7           0      1         0\n",
       "8           0      0         0\n",
       "9           0      0         1\n",
       "10          1      2         0\n",
       "11          1      2         1\n",
       "12          1      0         0\n",
       "13          1      0         1\n",
       "14          1      1         0\n",
       "15          1      1         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.drop(['company','job','degree'],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b6dc9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     1\n",
       "3     1\n",
       "4     0\n",
       "5     1\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     1\n",
       "10    1\n",
       "11    1\n",
       "12    1\n",
       "13    1\n",
       "14    1\n",
       "15    1\n",
       "Name: salary_more_then_100k, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc3b9a5",
   "metadata": {},
   "source": [
    "# Splitting into train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a054735a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=43)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb11f05a",
   "metadata": {},
   "source": [
    "# Decision Tree Classification Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15378ff4",
   "metadata": {},
   "source": [
    "### Instantiating and fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d7c31d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88c80818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ab1f84",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6a2a257",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:75.00%\n",
      "F1 Score: 0.8\n"
     ]
    }
   ],
   "source": [
    "y_pred = dtc.predict(X_test)\n",
    "from sklearn.metrics import accuracy_score,f1_score\n",
    "print(\"Accuracy Score:{:.2f}%\".format(accuracy_score(y_test,y_pred)*100))\n",
    "print(\"F1 Score:\",f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0078e16",
   "metadata": {},
   "source": [
    "# Predicting the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b43829b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company                            google\n",
       "job                      business manager\n",
       "degree                          bachelors\n",
       "salary_more_then_100k                   1\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703ceb5f",
   "metadata": {},
   "source": [
    "## Checking if Business Manager in Google has salary more than 100k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "05266f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_n    2\n",
       "job_n        0\n",
       "degree_n     0\n",
       "Name: 2, dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "405860da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f77e5f1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salary is more than 100k\n"
     ]
    }
   ],
   "source": [
    "if dtc.predict([[2,0,0]]) == 1:\n",
    "    print(\"Salary is more than 100k\")\n",
    "else:\n",
    "    print(\"Salary is less than 100k\")"
   ]
  }
 ],
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